Transcribing Against Time

TL;DR

This paper presents a dynamic, adaptive framework for correcting speech transcripts efficiently within a fixed time, significantly improving error correction rates by modeling transcriber effort and adapting to changing conditions.

Contribution

It introduces a real-time, transcriber-adaptive cost model training approach that enhances correction efficiency and captures dynamic factors affecting transcriber performance.

Findings

Efficiency improved by 15% on average

Up to 42% improvement for certain transcribers

Dynamic models capture fatigue and topic familiarity effects

Abstract

We investigate the problem of manually correcting errors from an automatic speech transcript in a cost-sensitive fashion. This is done by specifying a fixed time budget, and then automatically choosing location and size of segments for correction such that the number of corrected errors is maximized. The core components, as suggested by previous research [1], are a utility model that estimates the number of errors in a particular segment, and a cost model that estimates annotation effort for the segment. In this work we propose a dynamic updating framework that allows for the training of cost models during the ongoing transcription process. This removes the need for transcriber enrollment prior to the actual transcription, and improves correction efficiency by allowing highly transcriber-adaptive cost modeling. We first confirm and analyze the improvements afforded by this method in a simulated study. We then conduct a realistic user study, observing efficiency improvements of 15% relative on average, and 42% for the participants who deviated most strongly from our initial, transcriber-agnostic cost model. Moreover, we find that our updating framework can capture dynamically changing factors, such as transcriber fatigue and topic familiarity, which we observe to have a large influence on the transcriber's working behavior.